Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters
•Deep object-based semantic change detection framework (ChangeOS) is proposed.•ChangeOS seamlessly integrates object-based image analysis and deep learning.•City-scale building damage assessment can be achieved within one minute.•A global-scale dataset is used to evaluate the effectiveness of Change...
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| Published in: | Remote sensing of environment Vol. 265; p. 112636 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Elsevier Inc
01.11.2021
Elsevier BV |
| Subjects: | |
| ISSN: | 0034-4257, 1879-0704 |
| Online Access: | Get full text |
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| Abstract | •Deep object-based semantic change detection framework (ChangeOS) is proposed.•ChangeOS seamlessly integrates object-based image analysis and deep learning.•City-scale building damage assessment can be achieved within one minute.•A global-scale dataset is used to evaluate the effectiveness of ChangeOS.•Two local-scale datasets are used to show its great generalization ability.
Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanitarian assistance and disaster response. For building damage assessment, strong feature representation and semantic consistency are the keys to obtaining a high accuracy. However, the conventional object-based image analysis (OBIA) framework using a patch-based convolutional neural network (CNN) can guarantee semantic consistency, but with weak feature representation, while the Siamese fully convolutional network approach has strong feature representation capabilities but is semantically inconsistent. In this paper, we propose a deep object-based semantic change detection framework, called ChangeOS, for building damage assessment. To seamlessly integrate OBIA and deep learning, we adopt a deep object localization network to generate accurate building objects, in place of the superpixel segmentation commonly used in the conventional OBIA framework. Furthermore, the deep object localization network and deep damage classification network are integrated into a unified semantic change detection network for end-to-end building damage assessment. This also provides deep object features that can supply an object prior to the deep damage classification network for more consistent semantic feature representation. Object-based post-processing is adopted to further guarantee the semantic consistency of each object. The experimental results obtained on a global scale dataset including 19 natural disaster events and two local scale datasets including the Beirut port explosion event and the Bata military barracks explosion event show that ChangeOS is superior to the currently published methods in speed and accuracy, and has a superior generalization ability for man-made disasters. |
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| AbstractList | Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanitarian assistance and disaster response. For building damage assessment, strong feature representation and semantic consistency are the keys to obtaining a high accuracy. However, the conventional object-based image analysis (OBIA) framework using a patch-based convolutional neural network (CNN) can guarantee semantic consistency, but with weak feature representation, while the Siamese fully convolutional network approach has strong feature representation capabilities but is semantically inconsistent. In this paper, we propose a deep object-based semantic change detection framework, called ChangeOS, for building damage assessment. To seamlessly integrate OBIA and deep learning, we adopt a deep object localization network to generate accurate building objects, in place of the superpixel segmentation commonly used in the conventional OBIA framework. Furthermore, the deep object localization network and deep damage classification network are integrated into a unified semantic change detection network for end-to-end building damage assessment. This also provides deep object features that can supply an object prior to the deep damage classification network for more consistent semantic feature representation. Object-based post-processing is adopted to further guarantee the semantic consistency of each object. The experimental results obtained on a global scale dataset including 19 natural disaster events and two local scale datasets including the Beirut port explosion event and the Bata military barracks explosion event show that ChangeOS is superior to the currently published methods in speed and accuracy, and has a superior generalization ability for man-made disasters. •Deep object-based semantic change detection framework (ChangeOS) is proposed.•ChangeOS seamlessly integrates object-based image analysis and deep learning.•City-scale building damage assessment can be achieved within one minute.•A global-scale dataset is used to evaluate the effectiveness of ChangeOS.•Two local-scale datasets are used to show its great generalization ability. Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanitarian assistance and disaster response. For building damage assessment, strong feature representation and semantic consistency are the keys to obtaining a high accuracy. However, the conventional object-based image analysis (OBIA) framework using a patch-based convolutional neural network (CNN) can guarantee semantic consistency, but with weak feature representation, while the Siamese fully convolutional network approach has strong feature representation capabilities but is semantically inconsistent. In this paper, we propose a deep object-based semantic change detection framework, called ChangeOS, for building damage assessment. To seamlessly integrate OBIA and deep learning, we adopt a deep object localization network to generate accurate building objects, in place of the superpixel segmentation commonly used in the conventional OBIA framework. Furthermore, the deep object localization network and deep damage classification network are integrated into a unified semantic change detection network for end-to-end building damage assessment. This also provides deep object features that can supply an object prior to the deep damage classification network for more consistent semantic feature representation. Object-based post-processing is adopted to further guarantee the semantic consistency of each object. The experimental results obtained on a global scale dataset including 19 natural disaster events and two local scale datasets including the Beirut port explosion event and the Bata military barracks explosion event show that ChangeOS is superior to the currently published methods in speed and accuracy, and has a superior generalization ability for man-made disasters. |
| ArticleNumber | 112636 |
| Author | Ma, Ailong Zhang, Liangpei Wang, Junjue Zhong, Yanfei Zheng, Zhuo |
| Author_xml | – sequence: 1 givenname: Zhuo orcidid: 0000-0003-1811-6725 surname: Zheng fullname: Zheng, Zhuo email: zhengzhuo@whu.edu.cn organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China – sequence: 2 givenname: Yanfei orcidid: 0000-0001-9446-5850 surname: Zhong fullname: Zhong, Yanfei email: zhongyanfei@whu.edu.cn organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China – sequence: 3 givenname: Junjue orcidid: 0000-0002-9500-3399 surname: Wang fullname: Wang, Junjue organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China – sequence: 4 givenname: Ailong surname: Ma fullname: Ma, Ailong organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China – sequence: 5 givenname: Liangpei orcidid: 0000-0001-6890-3650 surname: Zhang fullname: Zhang, Liangpei organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China |
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| Cites_doi | 10.1016/j.isprsjprs.2009.06.004 10.3390/rs12172839 10.1016/j.rse.2018.11.014 10.1016/j.isprsjprs.2020.04.019 10.1016/j.rse.2020.111693 10.1016/j.rse.2021.112308 10.1037/0033-295X.84.4.327 10.1016/j.rse.2019.111593 10.1109/TGRS.2009.2038274 10.1016/j.isprsjprs.2018.02.014 10.1016/j.isprsjprs.2021.04.021 10.3390/geosciences10050177 10.3390/rs6064870 10.1007/BF02989909 10.1016/j.rse.2018.06.034 10.1016/j.isprsjprs.2013.06.011 10.1109/TGRS.2016.2601622 10.1016/j.rse.2018.04.050 10.1016/j.isprsjprs.2011.12.004 10.1016/j.isprsjprs.2020.12.009 10.1142/S1793431107000122 10.1061/(ASCE)1527-6988(2006)7:2(94) |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier Inc. Copyright Elsevier BV Nov 2021 |
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| References | Blaschke (bib0005) 2010; 65 Wu, Otoo, Shoshani (bib0150) 2005 Lee, Xu, Sohn, Lu, Berthelot, Gur, Khaitan, Koupparis, Kowatsch (bib0080) 2020 Zhang, Sargent, Pan, Li, Gardiner, Hare, Atkinson (bib0175) 2019; 221 Zheng, Zhong, Wang, Ma (bib0200) 2020 Chen, Zhu, Papandreou, Schroff, Adam (bib0015) 2018 Koshimura, Moya, Mas, Bai (bib0070) 2020; 5 Ge, Gokon, Meguro (bib0035) 2020; 240 Tversky (bib0135) 1977; 84 Zhong, Han, Zhang (bib0205) 2018; 138 Kelman (bib0065) 2003 Milletari, Navab, Ahmadi (bib0105) 2016 Yamazaki, Matsuoka (bib0155) 2007; 1 Valentijn, Margutti, van den Homberg, Laaksonen (bib0140) 2020; 12 Tong, Hong, Liu, Zhang, Xie, Li, Yang, Wang, Bao (bib0130) 2012; 68 Zhang, Sargent, Pan, Li, Gardiner, Hare, Atkinson (bib0170) 2018; 216 Simonyan, Zisserman (bib0125) 2014 Krizhevsky, Sutskever, Hinton (bib0075) 2012 Dong, Shan (bib0025) 2013; 84 Gupta, Goodman, Patel, Hosfelt, Sajeev, Heim, Doshi, Lucas, Choset, Gaston (bib0045) 2019 Gupta, Hosfelt, Sajeev, Patel, Goodman, Doshi, Heim, Choset, Gaston (bib0050) 2019 Yusuf, Matsuoka, Yamazaki (bib0160) 2001; 29 Cheng, Zhou, Han (bib0020) 2016; 54 He, Zhang, Ren, Sun (bib0055) 2016 Liu, Chen, Ma, Zhong, Fang, Xu (bib0095) 2020 Long, Shelhamer, Darrell (bib0100) 2015 Vickery, Skerlj, Lin, Twisdale, Young, Lavelle (bib0145) 2006; 7 Durnov (bib0030) 2020 Ronneberger, Fischer, Brox (bib0120) 2015 Lin, Dollár, Girshick, He, Hariharan, Belongie (bib0085) 2017 Zhang, Yuan, Li, Sun, Zhang (bib0180) 2021; 177 Liu, Yang, Lunga (bib0090) 2021; 256 Noh, Hong, Han (bib0110) 2015 Grünthal (bib0040) 1998 Plank (bib0115) 2014; 6 Zhang, Harrison, Pan, Li, Sargent, Atkinson (bib0165) 2020; 237 Brunner, Lemoine, Bruzzone (bib0010) 2010; 48 Zheng, Ma, Zhang, Zhong (bib0185) 2021; 174 Huang, Zhao, Song (bib0060) 2018; 214 Zheng, Zhong, Ma, Han, Zhao, Liu, Zhang (bib0190) 2020; 166 Zheng, Zhong, Ma, Zhang (bib0195) 2020 Wu (10.1016/j.rse.2021.112636_bib0150) 2005 Brunner (10.1016/j.rse.2021.112636_bib0010) 2010; 48 Zhang (10.1016/j.rse.2021.112636_bib0175) 2019; 221 Huang (10.1016/j.rse.2021.112636_bib0060) 2018; 214 Ronneberger (10.1016/j.rse.2021.112636_bib0120) 2015 Long (10.1016/j.rse.2021.112636_bib0100) 2015 Zheng (10.1016/j.rse.2021.112636_bib0185) 2021; 174 Zhang (10.1016/j.rse.2021.112636_bib0165) 2020; 237 Zheng (10.1016/j.rse.2021.112636_bib0200) 2020 Zhang (10.1016/j.rse.2021.112636_bib0180) 2021; 177 Cheng (10.1016/j.rse.2021.112636_bib0020) 2016; 54 Zheng (10.1016/j.rse.2021.112636_bib0195) 2020 He (10.1016/j.rse.2021.112636_bib0055) 2016 Dong (10.1016/j.rse.2021.112636_bib0025) 2013; 84 Milletari (10.1016/j.rse.2021.112636_bib0105) 2016 Tong (10.1016/j.rse.2021.112636_bib0130) 2012; 68 Liu (10.1016/j.rse.2021.112636_bib0090) 2021; 256 Ge (10.1016/j.rse.2021.112636_bib0035) 2020; 240 Durnov (10.1016/j.rse.2021.112636_bib0030) 2020 Valentijn (10.1016/j.rse.2021.112636_bib0140) 2020; 12 Lee (10.1016/j.rse.2021.112636_bib0080) 2020 Noh (10.1016/j.rse.2021.112636_bib0110) 2015 Chen (10.1016/j.rse.2021.112636_bib0015) 2018 Yamazaki (10.1016/j.rse.2021.112636_bib0155) 2007; 1 Zhang (10.1016/j.rse.2021.112636_bib0170) 2018; 216 Blaschke (10.1016/j.rse.2021.112636_bib0005) 2010; 65 Gupta (10.1016/j.rse.2021.112636_bib0050) 2019 Simonyan (10.1016/j.rse.2021.112636_bib0125) 2014 Koshimura (10.1016/j.rse.2021.112636_bib0070) 2020; 5 Gupta (10.1016/j.rse.2021.112636_bib0045) 2019 Lin (10.1016/j.rse.2021.112636_bib0085) 2017 Zhong (10.1016/j.rse.2021.112636_bib0205) 2018; 138 Zheng (10.1016/j.rse.2021.112636_bib0190) 2020; 166 Grünthal (10.1016/j.rse.2021.112636_bib0040) 1998 Krizhevsky (10.1016/j.rse.2021.112636_bib0075) 2012 Vickery (10.1016/j.rse.2021.112636_bib0145) 2006; 7 Liu (10.1016/j.rse.2021.112636_bib0095) 2020 Plank (10.1016/j.rse.2021.112636_bib0115) 2014; 6 Yusuf (10.1016/j.rse.2021.112636_bib0160) 2001; 29 Kelman (10.1016/j.rse.2021.112636_bib0065) 2003 Tversky (10.1016/j.rse.2021.112636_bib0135) 1977; 84 |
| References_xml | – volume: 240 start-page: 111693 year: 2020 ident: bib0035 article-title: A review on synthetic aperture radar-based building damage assessment in disasters publication-title: Remote Sens. Environ. – year: 2019 ident: bib0050 article-title: xbd: A Dataset for Assessing Building Damage From Satellite Imagery – volume: 216 start-page: 57 year: 2018 end-page: 70 ident: bib0170 article-title: An object-based convolutional neural network (ocnn) for urban land use classification publication-title: Remote Sens. Environ. – year: 2014 ident: bib0125 article-title: Very Deep Convolutional Networks for Large-Scale Image Recognition – volume: 84 start-page: 327 year: 1977 ident: bib0135 article-title: Features of similarity publication-title: Psychol. Rev. – volume: 214 start-page: 73 year: 2018 end-page: 86 ident: bib0060 article-title: Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery publication-title: Remote Sens. Environ. – volume: 68 start-page: 13 year: 2012 end-page: 27 ident: bib0130 article-title: Building-damage detection using pre-and post-seismic high-resolution satellite stereo imagery: a case study of the may 2008 Wenchuan earthquake publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 54 start-page: 7405 year: 2016 end-page: 7415 ident: bib0020 article-title: Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 7 start-page: 94 year: 2006 end-page: 103 ident: bib0145 article-title: Hazus-mh hurricane model methodology. ii: damage and loss estimation publication-title: Nat. Hazards Rev. – volume: 138 start-page: 281 year: 2018 end-page: 294 ident: bib0205 article-title: Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery publication-title: ISPRS J. Photogramm. Remote Sens. – year: 2020 ident: bib0080 article-title: Assessing Post-Disaster Damage From Satellite Imagery Using Semi-Supervised Learning Techniques – volume: 174 start-page: 254 year: 2021 end-page: 264 ident: bib0185 article-title: Deep multisensor learning for missing-modality all-weather mapping publication-title: ISPRS J. Photogramm. Remote Sens. – year: 2003 ident: bib0065 article-title: Physical Flood Vulnerability of Residential Properties in Coastal, Eastern England – volume: 5 start-page: 177 year: 2020 ident: bib0070 article-title: Tsunami damage detection with remote sensing: a review publication-title: Geosciences – volume: 1 start-page: 193 year: 2007 end-page: 210 ident: bib0155 article-title: Remote sensing technologies in post-disaster damage assessment publication-title: J. Earthq. Tsunami – volume: 166 start-page: 1 year: 2020 end-page: 14 ident: bib0190 article-title: Hynet: hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery publication-title: ISPRS J. Photogramm. Remote Sens. – start-page: 770 year: 2016 end-page: 778 ident: bib0055 article-title: Deep residual learning for image recognition publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 3431 year: 2015 end-page: 3440 ident: bib0100 article-title: Fully convolutional networks for semantic segmentation publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 12 start-page: 2839 year: 2020 ident: bib0140 article-title: Multi-hazard and spatial transferability of a cnn for automated building damage assessment publication-title: Remote Sens. – start-page: 234 year: 2015 end-page: 241 ident: bib0120 article-title: U-net: convolutional networks for biomedical image segmentation publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – start-page: 1520 year: 2015 end-page: 1528 ident: bib0110 article-title: Learning deconvolution network for semantic segmentation publication-title: Proceedings of the IEEE International Conference on Computer Vision – start-page: 565 year: 2016 end-page: 571 ident: bib0105 article-title: V-net: fully convolutional neural networks for volumetric medical image segmentation publication-title: 2016 Fourth International Conference on 3D Vision (3DV) – year: 2020 ident: bib0095 article-title: Multiscale u-shaped cnn building instance extraction framework with edge constraint for high-spatial-resolution remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 1965 year: 2005 end-page: 1976 ident: bib0150 article-title: Optimizing connected component labeling algorithms publication-title: Medical Imaging 2005: Image Processing. vol. 5747 – volume: 237 start-page: 111593 year: 2020 ident: bib0165 article-title: Scale sequence joint deep learning (ss-jdl) for land use and land cover classification publication-title: Remote Sens. Environ. – volume: 177 start-page: 161 year: 2021 end-page: 173 ident: bib0180 article-title: Combined deep prior with low-rank tensor svd for thick cloud removal in multitemporal images publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 221 start-page: 173 year: 2019 end-page: 187 ident: bib0175 article-title: Joint deep learning for land cover and land use classification publication-title: Remote Sens. Environ. – start-page: 801 year: 2018 end-page: 818 ident: bib0015 article-title: Encoder-decoder with atrous separable convolution for semantic image segmentation publication-title: Proceedings of the European Conference on Computer Vision (ECCV) – volume: 48 start-page: 2403 year: 2010 end-page: 2420 ident: bib0010 article-title: Earthquake damage assessment of buildings using vhr optical and sar imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 29 start-page: 17 year: 2001 end-page: 22 ident: bib0160 article-title: Damage assessment after 2001 gujarat earthquake using landsat-7 satellite images publication-title: J. Indian Soc. Remote Sens. – volume: 65 start-page: 2 year: 2010 end-page: 16 ident: bib0005 article-title: Object based image analysis for remote sensing publication-title: ISPRS J. Photogramm. Remote Sens. – year: 1998 ident: bib0040 article-title: European Macroseismic Scale 1998 – volume: 256 start-page: 112308 year: 2021 ident: bib0090 article-title: Change detection using deep learning approach with object-based image analysis publication-title: Remote Sens. Environ. – start-page: 1097 year: 2012 end-page: 1105 ident: bib0075 article-title: Imagenet classification with deep convolutional neural networks publication-title: Advances in Neural Information Processing Systems – start-page: 2117 year: 2017 end-page: 2125 ident: bib0085 article-title: Feature pyramid networks for object detection publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 6 start-page: 4870 year: 2014 end-page: 4906 ident: bib0115 article-title: Rapid damage assessment by means of multi-temporal sar-a comprehensive review and outlook to sentinel-1 publication-title: Remote Sens. – start-page: 4096 year: 2020 end-page: 4105 ident: bib0200 article-title: Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – year: 2020 ident: bib0195 article-title: Fpga: fast patch-free global learning framework for fully end-to-end hyperspectral image classification publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 10 year: 2019 end-page: 17 ident: bib0045 article-title: Creating xbd: a dataset for assessing building damage from satellite imagery publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops – volume: 84 start-page: 85 year: 2013 end-page: 99 ident: bib0025 article-title: A comprehensive review of earthquake-induced building damage detection with remote sensing techniques publication-title: ISPRS J. Photogramm. Remote Sens. – year: 2020 ident: bib0030 article-title: xview2 First Place Solution – volume: 65 start-page: 2 issue: 1 year: 2010 ident: 10.1016/j.rse.2021.112636_bib0005 article-title: Object based image analysis for remote sensing publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2009.06.004 – volume: 12 start-page: 2839 issue: 17 year: 2020 ident: 10.1016/j.rse.2021.112636_bib0140 article-title: Multi-hazard and spatial transferability of a cnn for automated building damage assessment publication-title: Remote Sens. doi: 10.3390/rs12172839 – volume: 221 start-page: 173 year: 2019 ident: 10.1016/j.rse.2021.112636_bib0175 article-title: Joint deep learning for land cover and land use classification publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.11.014 – start-page: 565 year: 2016 ident: 10.1016/j.rse.2021.112636_bib0105 article-title: V-net: fully convolutional neural networks for volumetric medical image segmentation – volume: 166 start-page: 1 year: 2020 ident: 10.1016/j.rse.2021.112636_bib0190 article-title: Hynet: hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2020.04.019 – volume: 240 start-page: 111693 year: 2020 ident: 10.1016/j.rse.2021.112636_bib0035 article-title: A review on synthetic aperture radar-based building damage assessment in disasters publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111693 – volume: 256 start-page: 112308 year: 2021 ident: 10.1016/j.rse.2021.112636_bib0090 article-title: Change detection using deep learning approach with object-based image analysis publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112308 – start-page: 770 year: 2016 ident: 10.1016/j.rse.2021.112636_bib0055 article-title: Deep residual learning for image recognition – year: 2020 ident: 10.1016/j.rse.2021.112636_bib0195 article-title: Fpga: fast patch-free global learning framework for fully end-to-end hyperspectral image classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 84 start-page: 327 issue: 4 year: 1977 ident: 10.1016/j.rse.2021.112636_bib0135 article-title: Features of similarity publication-title: Psychol. Rev. doi: 10.1037/0033-295X.84.4.327 – volume: 237 start-page: 111593 year: 2020 ident: 10.1016/j.rse.2021.112636_bib0165 article-title: Scale sequence joint deep learning (ss-jdl) for land use and land cover classification publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.111593 – start-page: 4096 year: 2020 ident: 10.1016/j.rse.2021.112636_bib0200 article-title: Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery – volume: 48 start-page: 2403 issue: 5 year: 2010 ident: 10.1016/j.rse.2021.112636_bib0010 article-title: Earthquake damage assessment of buildings using vhr optical and sar imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2009.2038274 – year: 2020 ident: 10.1016/j.rse.2021.112636_bib0095 article-title: Multiscale u-shaped cnn building instance extraction framework with edge constraint for high-spatial-resolution remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. – year: 2020 ident: 10.1016/j.rse.2021.112636_bib0030 – volume: 138 start-page: 281 year: 2018 ident: 10.1016/j.rse.2021.112636_bib0205 article-title: Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.02.014 – volume: 177 start-page: 161 year: 2021 ident: 10.1016/j.rse.2021.112636_bib0180 article-title: Combined deep prior with low-rank tensor svd for thick cloud removal in multitemporal images publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2021.04.021 – start-page: 1520 year: 2015 ident: 10.1016/j.rse.2021.112636_bib0110 article-title: Learning deconvolution network for semantic segmentation – year: 1998 ident: 10.1016/j.rse.2021.112636_bib0040 – volume: 5 start-page: 177 issue: 1 year: 2020 ident: 10.1016/j.rse.2021.112636_bib0070 article-title: Tsunami damage detection with remote sensing: a review publication-title: Geosciences doi: 10.3390/geosciences10050177 – start-page: 3431 year: 2015 ident: 10.1016/j.rse.2021.112636_bib0100 article-title: Fully convolutional networks for semantic segmentation – start-page: 2117 year: 2017 ident: 10.1016/j.rse.2021.112636_bib0085 article-title: Feature pyramid networks for object detection – volume: 6 start-page: 4870 issue: 6 year: 2014 ident: 10.1016/j.rse.2021.112636_bib0115 article-title: Rapid damage assessment by means of multi-temporal sar-a comprehensive review and outlook to sentinel-1 publication-title: Remote Sens. doi: 10.3390/rs6064870 – start-page: 10 year: 2019 ident: 10.1016/j.rse.2021.112636_bib0045 article-title: Creating xbd: a dataset for assessing building damage from satellite imagery – year: 2019 ident: 10.1016/j.rse.2021.112636_bib0050 – volume: 29 start-page: 17 issue: 1–2 year: 2001 ident: 10.1016/j.rse.2021.112636_bib0160 article-title: Damage assessment after 2001 gujarat earthquake using landsat-7 satellite images publication-title: J. Indian Soc. Remote Sens. doi: 10.1007/BF02989909 – volume: 216 start-page: 57 year: 2018 ident: 10.1016/j.rse.2021.112636_bib0170 article-title: An object-based convolutional neural network (ocnn) for urban land use classification publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.06.034 – start-page: 801 year: 2018 ident: 10.1016/j.rse.2021.112636_bib0015 article-title: Encoder-decoder with atrous separable convolution for semantic image segmentation – year: 2003 ident: 10.1016/j.rse.2021.112636_bib0065 – volume: 84 start-page: 85 year: 2013 ident: 10.1016/j.rse.2021.112636_bib0025 article-title: A comprehensive review of earthquake-induced building damage detection with remote sensing techniques publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2013.06.011 – volume: 54 start-page: 7405 issue: 12 year: 2016 ident: 10.1016/j.rse.2021.112636_bib0020 article-title: Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2601622 – volume: 214 start-page: 73 year: 2018 ident: 10.1016/j.rse.2021.112636_bib0060 article-title: Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.04.050 – year: 2014 ident: 10.1016/j.rse.2021.112636_bib0125 – volume: 68 start-page: 13 year: 2012 ident: 10.1016/j.rse.2021.112636_bib0130 article-title: Building-damage detection using pre-and post-seismic high-resolution satellite stereo imagery: a case study of the may 2008 Wenchuan earthquake publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2011.12.004 – start-page: 1965 year: 2005 ident: 10.1016/j.rse.2021.112636_bib0150 article-title: Optimizing connected component labeling algorithms – volume: 174 start-page: 254 year: 2021 ident: 10.1016/j.rse.2021.112636_bib0185 article-title: Deep multisensor learning for missing-modality all-weather mapping publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2020.12.009 – year: 2020 ident: 10.1016/j.rse.2021.112636_bib0080 – start-page: 1097 year: 2012 ident: 10.1016/j.rse.2021.112636_bib0075 article-title: Imagenet classification with deep convolutional neural networks – volume: 1 start-page: 193 issue: 03 year: 2007 ident: 10.1016/j.rse.2021.112636_bib0155 article-title: Remote sensing technologies in post-disaster damage assessment publication-title: J. Earthq. Tsunami doi: 10.1142/S1793431107000122 – start-page: 234 year: 2015 ident: 10.1016/j.rse.2021.112636_bib0120 article-title: U-net: convolutional networks for biomedical image segmentation – volume: 7 start-page: 94 issue: 2 year: 2006 ident: 10.1016/j.rse.2021.112636_bib0145 article-title: Hazus-mh hurricane model methodology. ii: damage and loss estimation publication-title: Nat. Hazards Rev. doi: 10.1061/(ASCE)1527-6988(2006)7:2(94) |
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| Snippet | •Deep object-based semantic change detection framework (ChangeOS) is proposed.•ChangeOS seamlessly integrates object-based image analysis and deep... Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using... |
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| SubjectTerms | Artificial neural networks Barracks Building damage Building damage assessment Change detection Classification Consistency Damage assessment Damage localization data collection Datasets Deep learning development aid Disaster management Disaster response Disasters environment humans Image analysis Image processing Image segmentation Information processing Localization Machine learning Man made disasters Natural disasters Neural networks OBIA Pattern recognition Post-production processing Remote sensing Representations Residential military buildings Semantics Spatial discrimination Spatial distribution Spatial resolution Statistical analysis statistics |
| Title | Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters |
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